Juntong Peng

Hi! I am a second-year Ph.D. student in Purdue ECE, co-advised by Prof. Yaobin Chen and Prof. Ziran Wang.

I received my bachelor degree in information engineering from Shanghai Jiao Tong University, advised by Prof. Siheng Chen. I have also spent a wonderful summer at the Department of Computer Science, Purdue University as a research intern, advised by Prof. Aniket Bera.

My research focuses on autonomous driving and computer vision, specifically on end-to-end driving and human-AI teaming. Previously, I worked on the efficiency aspect of collaborative perception and making use of the complementary strengths of agents in heterogeneous systems.

selected publications

CVPR

Communication-Efficient Collaborative Perception via Information Filling with Codebook

Yue Hu, Juntong Peng, Sifei Liu, and 3 more authors

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

Abs arXiv

We proposed a Communication-Efficient collaboration method, which enhances the tradeoff between performance and communication cost by optimizing information selection and representation.
RA-L

Graph-based Decentralized Task Allocation for Multi-Robot Target Localization

Juntong Peng, Hrishikesh Viswanath, and Aniket Bera

IEEE Robotics and Automation Letters, 2024

Abs arXiv

We proposed a graph neural network based method to dynamically allocate tasks to different robots. We utilized the comparative advantages of different robots to improve the overall performance of the system.
Preprint

Communication-Efficient Multi-Agent 3D Detection via Hybrid Collaboration

Yue Hu, Juntong Peng, Yunqiao Yang, and 4 more authors

2025

Abs arXiv

We utilized the confidence and uncertainty of single perception to guide the information sharing process, combining the benefits of both sparse and dense information form. Our method consumes far less communication resources than the previous SOTA.
ICDM

Compatible Transformer for Irregularly Sampled Multivariate Time Series

Yuxi Wei, Juntong Peng, Tong He, and 4 more authors

In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2023

Abs arXiv Code

We proposed a transformer-based model to handle irregularly sampled multivariate time series. Our model is inspired by the correlation between multi-agent trajectories.